Abstract In recent years the manufacturing industries are currently focused on adopting optimal hybrid mass/customized strategies. Working solely on customized production or mass production is not a desirable strategy for many manufacturers anymore. The rapid advances in technology, applications, and customers’ requirements are increasing the competitiveness and, therefore, productivity. Given this new scenario, the integration between production and delivery with parallel batching machines comes as an essential issue in logistics. Efficient integrated production and delivery operation is a critical factor for minimizing costs and increasing customer satisfaction. The particular case discussed in this paper consists of scheduling jobs of generic sizes and processing times on identical and parallel batching machines, aiming to maximize profits. The objective is to get a joint production and delivery schedule that maximizes total profits, focusing on attending consumers on time. We propose a mathematical formulation and show that the addition of a set of constraints improves the commercial solver performance. Additionally, we focus on the development and analys is of two fast polynomial methods. The first, RKP, aims to obtain relaxed solutions, while the last, CMFFDA, aims to obtain feasible solutions. Furthermore, computational experiments and sensitivity analysis with random instances of different sizes are conducted to evaluate the proposed methods and the integrated system. The results show tight gaps for the mathematical formulation and algorithms RKP and CMFFDA. The proposed algorithms present short computational times for small, moderate, and large size instances, an essential quality for daily operation.

中文翻译：

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使用通用作业尺寸和处理时间的并行配料机在生产和交付之间进行集成的问题

摘要 近年来，制造业目前专注于采用最佳的混合大规模/定制策略。对许多制造商而言，仅从事定制生产或批量生产不再是理想的策略。技术、应用和客户需求的快速进步正在提高竞争力，从而提高生产力。鉴于这种新情况，生产和交付与并行配料机之间的集成成为物流中的一个基本问题。高效的集成生产和交付运营是最小化成本和提高客户满意度的关键因素。本文讨论的特殊情况包括在相同和并行批处理机上调度通用大小和处理时间的作业，旨在最大化利润。目标是获得一个联合生产和交付计划，以最大限度地提高总利润，专注于准时吸引消费者。我们提出了一个数学公式，并表明添加一组约束可以提高商业求解器的性能。此外，我们专注于开发和分析两种快速多项式方法。第一个，RKP，旨在获得宽松的解决方案，而最后一个，CMFFDA，旨在获得可行的解决方案。此外，对不同大小的随机实例进行了计算实验和敏感性分析，以评估所提出的方法和集成系统。结果表明，RKP 和 CMFFDA 的数学公式和算法存在很大差距。所提出的算法为小型、中等和大型实例提供较短的计算时间，